[PPT] - CS-184: Computer Graphics Lecture #19: Motion Capture Prof. James PowerPoint Presentation

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CS-184: Computer Graphics

Lecture #19: Motion Capture

Prof. James O’Brien

University of California, Berkeley

V2008-S-19-1.0

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Today

Motion Capture

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Motion Capture

Record motion from physical objects Use motion to animate virtual objects

Simplified Pipeline:

Setup and calibrate equipment Record performance Process motion data Generate animation

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Basic Pipeline

From Rose, et al., 1998

Setup Record Process Animation

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What types of objects?

Human, whole body Portions of body Facial animation Animals Puppets Other objects

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Capture Equipment

Passive Optical

Reflective markers IR (typically) illumination Special cameras

Fast, high res., filters

Triangulate for positions

Images from Motion Analysis

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Capture Equipment

Passive Optical Advantages

Accurate May use many markers No cables High frequency

Disadvantages

Requires lots of processing Expensive systems Occlusions Marker swap Lighting / camera limitations

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Capture Equipment

Active Optical

Similar to passive but uses LEDs Blink IDs, no marker swap Number of markers trades off w/ frame rate

Phoenix Technology Phase Space

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Capture Equipment

Magnetic Trackers

Transmitter emits field Trackers sense field Trackers report position and

rientation

Control May be wireless

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Capture Equipment

Electromagnetic Advantages

6 DOF data No occlusions Less post processing Cheaper than optical

Disadvantages

Cables Problems with metal objects Low(er) frequency Limited range Limited number of trackers

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Capture Equipment

Electromechanical

Analogus

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Capture Equipment

Puppets

Digital Image Design

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Performance Capture

Many studios regard Motion Capture as evil

Synonymous with low quality motion No directive / creative control Cheap

Performance Capture is different

Use mocap device as an expressive input device Similar to digital music and MIDI keyboards

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Manipulating Motion Data

Basic tasks

Adjusting Blending Transitioning Retargeting

Building graphs

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Nature of Motion Data

Witkin and Popovic, 1995

Subset of motion curves from captured walking motion.

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Adjusting

IK on single frames will not work

Gleicher, SIGGRAPH 98

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Adjusting

Define desired motion function in parts

Result after adjustment Inital sampled data Adjustment

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Adjusting

Select adjustment function from “some nice space”

Example C2 B-splines

Spread modification over reasonable period

f time

User selects support radius

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Adjusting

Witkin and Popovic SIGGRAPH 95

IK uses control points

f the B-spline now

Example: position racket fix right foot fix left toes balance

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Adjusting

Witkin and Popovic SIGGRAPH 95

What if adjustment periods overlap?

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Blending

Given two motions make a motion that combines qualities of both Assume same DOFs Assume same parameter mappings

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Blending

Consider blending slow-walk and fast-walk

Bruderlin and Williams, SIGGRAPH 95

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Blending

Define timewarp functions to align features in motion

Normalized time is w

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Blending

Blend in normalized time Blend playback rate

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Blending

Blending may still break features in original motions

Touchdown for Run Touchdown for Walk Blend misses ground and floats

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Touchdown for Run Touchdown for Walk

Blending

Add explicit constrains to key points

Enforce with IK over time

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Blending / Adjustment

Short edits will tend to look acceptable Longer ones will often exhibit problems Optimize to improve blends / adjustments

Add quality metric on adjustment Minimize accelerations / torques Explicit smoothness constraints Other criteria...

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Multivariate Blending

Extend blending to multivariate interpolation

"Speed"

“Speed” “Happiness”

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"Speed"

If we have other examples place them in the space also

Multivariate Blending

Extend blending to multivariate interpolation

“Speed” “Happiness”

Use standard scattered-data interpolation methods

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Transitions

Transition from one motion to another

Perform blend in overlap region

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Cyclification

Special case of transitioning Both motions are the same Need to modify beginning and end of a motion simultaneously

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Transition Graphs

Flip Stand Run Walk Sit Trip Dance

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Motion Graphs

Hand build motion graphs often used in games

Significant amount of work required Limited transitions by design

Motion graphs can also be built automatically

Flip Stand Run Walk Sit Trip Dance

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Motion Graphs

Similarity metric

Measurement of how similar two frames of motion are

Based on joint angles or point positions Must include some measure of velocity Ideally independent of capture setup and skeleton

Capture a “large” database of motions

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Motion Graphs

Compute similarity metric between all pairs

f frames

Maybe expensive Preprocessing step There may be too many good edges

Clustering Walking , frame i Running, frame j

Arikan and Forsyth, 2002

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Motion Graphs

Random walks

Start in some part of the graph and randomly make transitions Avoid dead ends Useful for “idling” behaviors

Transitions

Use blending algorithm we discussed

Domain of smoothing Smoothed Signal

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Motion graphs

Match imposed requirements

Start at a particular location End at a particular location Pass through particular pose Can be solved using dynamic programing Efficiency issues may require approximate solution Notion of “goodness” of a solution

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